Prompt Engineering Full Course 2026 | Prompt Engineering Tutorial For Beginners | Simplilearn

Key Takeaways

Welcome to prompt engineering full course by simply learn. Do you want to learn one of the most exciting skills in AI? Then prompt engineering is how you talk to AI models like chart GPD claude Gemini to get the perfect answers. So whether it's for writing code, creating content or solving problems. In this course, we will show you how this skill is changing everything. Helping marketers create better ads, helping developers write clean code, and making work more creative and efficient everywhere. We'll start with the basic what prompts are, how to write them well, and easy framework you can use. Then we'll dive into advanced tips and test prompt with real tools. And by the end, you will know the prompt engineering isn't just about asking questions. It's about giving super instructions to make your ideas come alive. Now before we commence just a quick information if you want to enhance your career in AI and machine learning then you should definitely check out Simply Learn's professional certificate in AI and machine learning with PDI University online and IBM that will help you to master key AI skills like chat GBT LMS deep learning and agentic AI through live classes and hands-on project. So in just six months you'll work on real world industry projects. You'll use 18 plus popular tools like Python and TensorFlow. And you can also earn certifications from Perurdu and IBM. You'll get career support including resume help, mock interviews and job assistance. So guys hurry up and enroll now and you can find the link below. >> Hello and welcome to CHB prompt engineering course. This course is designed to equip you with the skills to craft effective prompts and optimize AI generated responses. We begin with an introduction to prompt engineering explaining its significance and how structured prompts improve AI interaction. Next, we will dive into the essential components of an effective prompt covering clarity, context, specificity, constraints, and examples to ensure precise and relevant outputs. Moving forward, we will explore some real world application and prompt patterns, demonstrating how prompt engineering enhances content creation, coding assistance, customer support, and data analysis. You will also learn different prompting techniques, including instruction based, exampled driven, fill-in- thelank, and step-by-step prompt. To help you refine your skills, we will discuss common prompting errors such as vague instruction, excessive detail, and poor formatting along with hands-on exercises to improve AI responses. Finally, we will cover advanced prompting techniques including zeros, few short, and chain of thought prompting which are essential for guiding AI through complex problem solving and improving accuracy in real world scenarios. By the end of this course, you will be able to write powerful structured prompts that maximize AI efficiency across various domain. So let's begin. The first is introduction to prompt engineering. So prompt engineering is a crucial skill in the realm of artificial intelligence especially when interacting with large language models like charge. It involves crafting well ststructured prompts to guide AI responses effectively. Whether for content creation, automation or problem solving. A well-gineered prompt ensures accurate and relevant output. Prompt engineering is not just about writing commands. It's about communicating intent clearly to the AI. The way a prompt is phrased can significantly influence the AI response. The more precise and structured prompt is, the better the result will be. This is why businesses, developers and content creators must invest time in the refining their prompt writing technique. So now let's see what is prompt engineering actually is. Prompt engineering refers to the process of designing and refining prompts to achieve desired responses from AI models. The effectiveness of AI generated content depends largely on how well the prompt is structured. By understanding how language model interprets, users can enhance AI's efficiency in performing various tasks. So let's take an example. Consider AI powered customer support chatboard. A poorly structured prompt will look like this. Help the customer with their issue. So here you can see it is giving answer like sure could you please provide the details of the customer issue such as the product or service they are facing problem with any other messages or any relevant order or account information if applicable. A well engineered prompt like this analyze the customer issue based on previous conversation history and suggest a step-by-step troubleshooting process before escalating to a human agent. So now it will analyze the customer issue based on the context we have and more specifics about their problem and it will give you a step-by-step troubleshooting process that can be followed as you can see on the image. So the AI now has clear instruction to follow improving the chances of generating a relevant and actionable response. So now let's move forward and see what is chatb is an advanced AI language model developed by open that uses natural language processing NLP to generate humanlike text responses. So it is based on the GPD generative pre-trained transformer architecture as you can see on the screen. So this GPT architecture which enables it to understand, interpret and generate text based on the user inputs. As you can see on the image I wrote here what is machine learning in one line. So it gave me the answer. So chat GP can assist in various task including answering questions, writing content, summarizing text, generating code and even engaging in interactive conversation. So powered by deep learning, Chat GPT predicts and generates text by analyzing vast amount of data making it highly effective for automation, brainstorming and problem solving. It supports multiple industries including education, customer support, content creation, and programming. So the model continuously improves through fine-tuning and user feedback allowing it to provide more relevant and accurate responses whether for personal or professional use. Chat GPT is a powerful AI assistant designed to enhance productivity and streamline communication. So in the next unit we will see essential components of effective prompts. So prompt engineering is the practice of carefully crafting inputs to guide an AI model toward generating precise, relevant and meaningful responses. So a well structured prompt can significantly improve the quality of output making AI responses more accurate, concise and aligned with user expectation. So to create an effective prompt you must consider several key elements including clarity, context, specificity, constraints and with different different examples. So each of these plays a vital role in shaping the AI's response ensuring that it meets the intended purpose. So now let's explore these elements in detail along with real world example to illustrate their importance. So the first one is clarity. So one of the most critical aspect of prompt engineering is clarity. A prompt should be direct, easy to understand and free from ambiguity. So when a prompt lacks clarity, the AI may misinterpret the request resulting in the vague or irrelevant responses. For example, a generic prompt like tell me about AI does not specify whether the user wants a historical overview application or technical details. So the AI might provide a long generalized response that does not align with the user's intent. So example instead of tell me about AI you can write provide a brief history of artificial intelligence including key milestone from the 1950s to the present or to the today. So why is this better? because it clearly defines the scope of the response like history of AI and it specifies a timeline which is 1950s to present and the phrase key milestones helps AI focus on significant developments rather than providing unnecessary detail. So by using a precise and structured prompt the response is more focused, informative and aligned with what the user actually needs. The second is context. Providing relevant background information without enough contextual information is wrong. So the AI may provide an answer that is either too broad or unrelated to the actual intent of the query. For instance, a simple prompt like summarize the article does not provide any details about the article topic, length or the key points to focus on. So the AI may generate a summary that does not highlight the aspect of the users is looking for. Instead of that you can write summarize the article on climate change highlighting the cause and effects in under 200 words. So why is this better? It specifies the subject of the article which is climate change. It directs the AI to focus on causes and effects rather than general information. and it limits the word count ensuring a concise summary. So by adding contextual details you ensure that AI tailors its response to your exact needs making it relevant and structured. The third is specificity. Please define the scope clearly. So specificity in prompt engineering helps narrow down the AI's response, ensuring that the output is concise and relevant rather than overly broad or off topic. A vague prompt can result in an unfocused response requiring additional clarification or refinement. For example, asking list some programming language gives the AI no direction regarding what kind of programming languages are required. So the AI might random languages so and some of which may not be relevant to the user's need. So instead of that you can write less five programming languages commonly used for web development along with their primary use cases. So why this prompt is better? It limits the number of languages to five ensuring a focus response. It specifies web development as a domain avoiding unrelated programming languages and it asks for primary use case making the response more informative and practical. So by defining the scope of your request clearly you eliminate guesswork and get precisely what you need from the AI. And the fourth is constraints which control output with limits. So constraints help structure the AI's response ensuring that the output is within the required word count format or tone. Without constraint response can be too long, too short or may not match the required style. For instance, a simple prompt like explain the renaissance does not indicate how detailed or structured the explanation should be. AI might generate a long detailed essay which might not be what the users want. So instead of that you can write like write a 100word summary of the Renaissance era in a formal tone. So why is this better? It limits the word count 100 word making the response concise. It specifies a formal tone ensuring an academic style response. It guides AI in producing an answer that fits the expected structure. So constraint are particularly useful in business research and content creation where precise formatting and style matter. So now let's jump into the demo part and see how it practically looks like. So yeah so I welcome to the demo part and I am using uh plus version of JP as of now as you can see the plus version and yeah so this is the charge interface and I hope everyone is aware of right so now let's see one by one what are the effective uh elements of the prompt okay which I have discussed already as you can see first is clarity and context constraints specificity with the examples which we discussed okay so let's see how practically it's look like right what are the answers GP is given okay and generated so the first one is clarity okay so here if I will write tell me about AI so a see so these are search results search if you will see the search it will search from the web okay otherwise it will give me from the it will generate automatic okay in which it it is 20 so yeah so if I will write just tell me about AI so let's see now what it will give okay if you can see it's giving AI artificial intelligence refers to the simulation of human intelligence and machine enabling them perform tasks artically They require human cognition. This is this types of AI it is giving. Okay. How AI works it is giving. Okay. And common application it is giving and then future of AI it is giving. Yeah. So future of AI common application how AI works and types of AI. But if you will write with proper clarity what do you want like provide history of artificial intelligence including key milestone from the 20 brother okay so here is so here you can see I'm describing clearly like the scope of the response so you can see history of AI okay this is the scope and timeline 1950s to present okay and the phrase key milestone okay it will help AI focus to significant developments rather than providing unnecessary detail. Okay. So let's see what are the results generate one title latest see 1950s the birth of AI 1950 Alan Turing introduces the Turing test in his paper computing machinery and intelligence like this in 1960 1978. This happened. Okay. 1980s the rise of export system. Okay. That is my for medical diagnosis. 1990 AI becomes practical. IBM's deep blue defeats. 2000 AI in everyday lives. In 2005 this happened. 2011 this happened. 2012, 13, 14, 16, 17, this happened. 2020 this happened. Okay. 2024 present AI continues evolving with LLOPS, autonomous AI agents and enhanced evolution. So this is the brief history of AI including key milestone. See these are the milestone. Okay. See the D the conference led by John McCarthy official coin the term artificial intelligence. So these are some key milestones you get okay in particular year. So this is how agility can give you answers if you will type your prompt clearly with clarity you can say right. If you want to discuss about more of particular you know topic or answer you can just write here like tell me more about after it will be more detail okay so now let's see the difference between this And this okay one liner here it will give more water. Okay. From here from here you can stop this as well. Right. So now next second comes context. So context is like providing relevant background information. Okay. So let me quickly first So now let me quickly first add one article for you. Okay. from the web. So as you can see we have I have just copy pasted my one article here. So now I will give a simple context okay like not even context okay I will give prompt without context sorry I will write here just summarize article just like this okay let's see what it will do okay the so first the title will come okay then climate this human So yeah, first let me tell you the summary should not be this big. Okay. So instead of just typing or writing or giving the this prompt summarize the article what you can write here instead of this you can write summarize the article on climate change. Climate change highlighting the causes and effects in under 200 words because no one want the summary this big, right? So send. So this is where you have set the context like it specifies the subject first climate change and it will direct the AI to focus on causes and effects. You can see here highlighting the causes and effect and the limits the word count ensuring a concise summary of this particular. See this is the length of new summary. If you will give a prompt with proper context right see here small into 200 words. Okay. So now let's move forward and see specificity which will define the scope more clearly. Okay. So if I will write here list some programming languages. Okay. Enter. So it will give the like AI no direction regarding what kind of programming languages are required to me. So it will just might give the list of random languages as you can see here or some of which may not be relevant to the user's need. Okay. What if I'm working on only this general purpose programming? I don't want data science programming languages but still it gave me I don't need mobile app development languages. So that is why you have to particularly specify which languages you want to use or you want right. So here you can write list five programming languages commonly used for web development along with their primary cases. Okay. Yeah. So now it will first limit the language to five. Okay. I have written five ensuring a focus response. And now it specifies only web development. That's why I'm saying see it gave me scripting automation game development system. I don't need that. So that's why it's very important in the prompt you have to specify. Okay. along with their primary use cases. So it will make the response more informative and practical. So by defining the scope of your request clearly, you will eliminate guesswork and get precisely what you need from J or AI by programming JavaScript with their use case. Right? So now let's move forward and see what are constraints how you can control output with your limits. Okay. So if I will write explain. So here you can see there is no guidance on length or tone. It will give me automatically how much it want. So here you can see there is no guidance on length or tone. Okay. But what if you will write like explain me 100 word summary or you can write screen [Music] about machine learning installable. to let's press enter. So here you can see it limits the word count to the 100%. Okay, making the response concise and it will specify a formal tone. I have written here a formal tone that will ensure an academic style response. Okay. And it will guide AI in producing an answer that fits the expected structure. Okay. So now first let's read this machine learning is a subset of artificial intelligence that enables computer to learn from data and improve their performance without being exclusive to the program. So here see machine learning is a branch of artificial intelligence that enables system to learn from data and improve performance without exclusive programming. It involves algorithm that identify data make prediction and automate. ML is categorized into proper formal tone. Right? So there is one more thing how you can use examples to define your problem. Right? If I will write explain Newton's third law, it is very two openended. Okay. So it gave me three examples jumping, rocket launch and walking. Okay. So now I will write here with proper examples mentioning proper examples. Okay. Explain third law and temper for instance mention topic launch. Okay. See first for every action there is an equal and opposite direction. So this is the third law. So here see in the case of rocket launch the rocket engine expel hot gases downward action force at high speed. But if you'll see here Newton's third law state for every action there is an equal and opposite action. So this means that when one object exhaust the force on another the second object exhaust. So here I have mentioned properly I need a rocket launch example. So this is how you can guide the chair GPT with sample responses. So effective prompt engineering maximizes the quality of AI generated responses by ensuring clarity, context, specificity, constraints or by giving an examples. So by following these principles, you can craft highquality prompts that yield more accurate structure and relevant responses. So what are the key takeaways? So clarity ensures AI understand the request without ambiguity. Context provides the background details to improve accuracy. Specificity narrow down the response to match user intent and constraint define structure length and style for better output and examples helps guide AI to produce responses in desired format. So by implementing these techniques you can optimize your prompt for more useful structured and efficient AI than responses. So in the next unit we will see some real world application of prompt engineering and learn about prompt patterns. Prompt engineering is widely used in various domains. So here are some of the key areas where it plays a crucial role. The first one is content generation. AI is leveraged to generate blog posts, write social media captions and create engaging stories. So here is one example. So an AI powered content writer can be prompted to generate a 500word article on the impact of AI in health by specifying the structure tone and the key points to include and why it matters. So a well structured prompt ensures the output is relevant, coherent and follows a logical flow making the content useful and engaging. And the second is coding assistance. So AIdriven coding assistants help programmers by generating code snippet and debugging support. So here is an example. So a developer can prompt the AI with like write a Python function to check if a given string is appalent or not. So the AI will then generate an efficient function following Python's best practices. So adding constants like optimize for performance and avoid using recursion can further improve the response by making the output more efficient. The third one is customer support. AI chatbots can be programmed to handle customer queries reducing human intervention. So a well structured prompt like provide a step-by-step troubleshooting guide for Wi-Fi connectivity issues on Windows 11 will yield a more effective respond compared to a web query. So best practices are it's like specifying constraints such as provide a response under 200 words and use simple language and short clarity and conciseness. And the fourth one is data analyst. So AI model can summarize reports and extract insights from large data sets. Example is like a financial analyst can use a prompt like summarize the key revenue trends in the company's Q4 report in three bullet point and the importance is so this will help extract key insights efficiently while keeping summary concise and actionable. The last one is education and learning. AI power tutors assist students by explaining concept in detail. So example is a prompt like explain the pythogan theorem using a real life example. Ensure AI provides an intuitive explanation with practical application. So the best approach is the response can be enhanced by adding use a story based analogy involving a right angle triangle. So now let's move to the demo part and see what are the different prompt patterns. So in this demo part we will talk about some different prompt patterns in prompt. Okay. So prompt engineering includes various pattern that helps structure interaction effectively. So there are some common patterns include like instruction based prompts and example driven prompts, fill in the blanks prompts and step-by-steps prompts. Okay. So these four are the main types. So yes, so now let's first start with instruction based prompt. So instructionbased prompts are like direct command which help generate specific responses, right? So these instruction based prompts are very effective for structured output ensuring clarity and precision. So the key is to be explicit in what you want AI to do such as summarizing, explaining or permitting content. So now let's see two three examples for each of the prompts. So like the first I will write here explain quantum mechanics in simple terms. Okay. So here AI will provide a beginner friendly explanation avoiding technical jargon. Okay. Because I have written here in simple terms. Correct. Let's enter. Now you can see here it gave me one definition and some of the key idea. Okay. and at the last conclusion. Okay. So this is the beginner friendly explanation as I told you before. So now let's do one more like if I want to write summarize this in one paragraph. Okay, this particular thing in one paragraph. So now AI will extract key points and deliver a concise version of this particular you can see quantum again see in only one paragraph. So these are the instruction based form because I have gave the instruction summarize. Okay here already I gave in simple terms right. Okay, one more. Let's try. I can write here. Write an email to my manager requesting for 2 days. Okay. So now AI will format a professional and politely request email. Okay. See hi manager name I hope you are doing well very polite I would like to request for mention day due to this this reason and I will ensure all of my tasks are up to date before my leave and will coordinate with the team if needed right so this is how instruction based forms work so this approach is very straightforward and works well for task based request where concise clear instructions are necessary So now let's move forward and see example driven prompts. So exampled prompts helps AI to understand the expected format and tone by providing one or more example before requesting a response. So this technique is very useful when generating creative content translation or stylic adaptions. So the AI learns from the patterns set by the examples and follows the same structure. Okay. So now if what if I will write generate poem about winter in the style of robot roster. Okay. So I have written generate a poem about winter in the style of robot ro. So now chat GPT will mimic fro style using nature inspired theme and vivid image. Okay, see it gave me two responses. I can select any of them. Okay. So here you can see the frost has crept where footballs fade upon the path through woods and see all the nature is coming. A hush now hangs on branch and boat as winter bent to pines deep and here as well. So the boards stand yeah what if I will choose this I prefer this response so it will come like this so it is mimicking process type so let's take another example and even you can write translate the following where you can write hello how are you to Spanish or don't write Spanish to cola. Okay, so it will understand. See, I just wrote cola. Okay, so it will understand. Hello, how are you? Transfer to Hola in Spanish. So chat GP will follow the same format for the further translation. If I will write translate my name is Sam. See me lu Sam is in Spanish. Right here I didn't give any language or any signs you can say. Okay, even you can give it prompts like reward reward this sentence like Shakespeareian monog. Okay. And I will give here the sun is shining brightly today. Okay. So here I have given the example here. I need this text and monologue. See the golden or do play with radiant might casting heavenly glow upon the earth so bright. Okay. So here Chad GPT transformed the sentence into an old English pros such as this only. Okay. Right. So this exampled driven promps pattern is effective when consistency and style needed to be maintained ensuring the AI produces responses in a predefined format. Right? If you will upload something just write child to charg that I want this type of output okay and ch will follow the same structure. So now let's move forward and see fill in the blanks prompts. So fill in the blank prompts encourage AI to complete a sentence or a paragraph based on context cues. So this method is often used for open-ended creative writing trivia or knowledge record. So these prompts allow AI to generate flexible yet structured responses guiding its output without being overly restrictive. Okay. So what if I will write my name is dash. Okay. Let's see. So in six style you would say h I'm call name by the name. Okay. So it took this right. What if I will write a black hole is formed when okay so this is the fill in the back. So a black hole is formed when a massive star reaches the end of its life and collapses under its own gravity. So charge will complete the sentence with a you know scientifically accurate statement right like this a black hole is formed when a massive star reaches this this this okay so it will automatically complete let's take one simple example like key to success in live is that Okay. So now chip will fill in the blank with different perspective. Okay. Like such as perseverance and continuous learning. Okay. So the key to success in life is a combination of persistence, continuous learning. See adaptability. Okay. With different different options or you can say perspectives. And what if I will give JGBT like to one or more you know fill in the blanks. So I will write if I could travel anywhere in the world I would go to dash because dash. Okay. So now charity will generate a destination and a logical reason why I will go there. So okay let's see if I could travel in the world I will go to Japan because it blends ancient tradition with modern innovation offering breathtaking landscape and has a rich culture heritage from serene temples and cherry blossoms to futuristic cities and incredible cus. Okay, here it gave me one destination and proper logical reason. So this pattern is especially useful for engaging users in interactive task making it a great technique for quizzes, educational materials and thoughtprovoking responses. Right. So now let's discuss the last one which is stepbystep prompt. So stepbystep prompts break down complex topics into multiple steps like a formula you can say like algorithm. So making them easier to understand. So these prompts are commonly used in technical explanation, process description and problem solving scenarios. So encouraging AI or chat to explain things in a sequential manner improves clarity and ensures the responses follows a logical flow. So it will be very easy for us as well to understand a particular thing right whether it's a 12th standard map or Pythagoras theorem or anything. Okay. So now let's uh sit describe the quarter cycle in four sequential steps. Okay. So it will give me see this is the first step right and this is the second this is the third step and this is the fourth step because we have added here in four sequential steps. Okay. So here is the list of four steps. First evaporation condensation precipitation and collection. Right? Explaining is each step as well. And even you can write x ask ask explain how to get just write here step by step. Okay. First ingredient it's giving the steps. If you want only steps and you know all these gradients you can just add here I need only steps. Okay. So these are some steps. So how to bake a cake and even you can ask charge to solve solve this math problem step by step. Okay. It's like 2x + 5 = 15 post. Okay. So now charge will break down all these step. Okay. See the step one subtract five from both sides. Then divide both sides by two. Then final answer is this x = 5. Okay. So this method is highly highly effective for explaining structured process and helps enhance AI generated educational content for learning based application. So each of these prompts serves a unique purpose in prompt engineering enhancing AI generated outputs based on the use cases. Whether you need a structured response, stylish consistency, open-ended creativity, or detailed explanation, selecting the right pattern can make AI responses more accurate, useful, and engable. So now let's move forward and see in the next module some common prompting errors and how you can overcome them. In this module, we will discuss about common prompting errors and how to fix them. So, chat GPT or AI generated responses depend heavily on how you craft your prompts. Even experienced users sometimes make mistakes leading to unclear or incomplete or confusing outputs. So, now let's go deeper into some of these common errors and how to refine prompts for better result. So first one is being too vague. So this is a problem. A vague prompt lacks enough detail making it difficult for the AI to provide a precise or useful answer without clear instruction. So the AI may generate a generic response that doesn't meet your needs. Okay. For example, if I will write tell me about the economy. Okay. So this itself it's very broad, right? And it is lacks direction as well. India's economy or US economy, right? Like this. So the economy is a vast topic covering GDP, inflation, employment, global trade and much more things. So the AI doesn't know or GRPD doesn't know which aspect you want to focus on if I need GDP, inflation or something else. So the response may be too general or not relevant to you know our actual query. Let's see what it gives. Okay. The economy refers to the system of production. This this is key aspects of the economy. types of economy right market economy, command economy, mix economy and the second thing is economy indicators like GDP, inflation rate, employment rate, economic cycle. But what if we don't need this? Okay. So how to fix it? So you can write explain the impact of inflation on consumer spending in the US. Okay. In 2025, right? So yeah. So now see in in 2025 inflation has significantly increased consumer spending patterns in the United States. Okay. And these are some sources from the web. Right here you can see charge gave anything but we never wanted this. Okay. So this is how you can fix the V problem and why this prompt is better than this prompt because it narrows the focus to inflation rather than the entire economy. Okay. and it specify the consumer spending unrelated details and it defines the region US as well and the time period 2025 for the relevant. So the more specific your question, the more relevant and actionable the JPT's response will be. Fine. So now let's move forward and see the second problem which is overloading with information. Fine. So this is a problem that includes too many details in a single request and can overwhelm the AI leading to an unfocused or incomplete response. So the AI may prioritize certain details while ignoring others. Let me give you an overload example like explain how blockchain works. including its history, advantages, disadvantages and comparison with centralized databases. So here you can see too many requests partic at the particular time right I need history I need mechanism I need advantage disadvantage and the comparison as well so too many requests add one so why is it a problem so the chat GP or AI might give service level answers for each topic instead of the explanation so the response may be too long or confusing to follow and the key point might get skipped right or you can say it will summarize poorly due to limited space. See only one one pointer. See only one pointer. How to fix them? Okay. So now you can write here explain instead of that explain how blockchain work in simple terms with one real world use. Yes. Okay. So now you can see the answer between them how blockchain works step by step process problem why blockchain is useful. So this is much better than that because the focus is only on how blockchain works rather than multiple aspects right and the request is to keep it simple and ensuring the clarity right and adding a use case makes the explanation more practical and relative right so here you get the problem of food supply chain offer suffer from broad and slow tracking of contaminated products. Right? So if you need more details, you can break your question into separate prompt like what is the history like this. What is the history of blockchain? You can ask once you can ask the advantage and disadvantage of blockchain separately. You can ask how does blockchain compare to centralized database stability because you will get the brief explanation of each point there. Fine. So the third error is ignoring output formatting right here. Ignoring output formatting. So what is this problem? If you don't specify how you want the response structured, so the AI might return it in unhelpful format. So this can make it harder to read or use the information effectively. Okay. So example of unstructured prompt is like less game benefits of exercise. Okay. So this like charg might generate a long paragraph instead of a clear list. I'm supposing. Okay. Let's see. Yeah. So now it gave kind of list but here key point could lost in the text instead of standing out right with these emojis and all. So how you can fix it? So you can just ask list the top five benefits of exercise in bullet points. Right? So now you guys will be wondering why this prompt is better than this prompt. So it ensures a concise structured answer like top five benefits of exercise in bullet point. So bullet point improve readability and clarity. So limiting it to five benefits makes the response more focus. See okay simple and short or you can say simple and twist better than this. Right? So you can specify other formats too like create a table comparing aerobic and anorobic exercise based on benefits and example or you can ask like explain how to start a workout routine in five simple steps or if you want in short paragraphs you can write summarize the benefits of strength training in two short paragraphs like this. So now let's see one more common error which is not refining prompts. Okay, so this is a problem where a single attempt may not give you the best response. So check or AI generated content can sometimes be too generic or too detailed or you can say slightly off topic. So refining your prompt improves accuracy of your prompt or your result. So let me give an example like generate sales page. Everyone know it's too broad and lacks of content. It's a problem because the charg doesn't know what product or service the pitch is for. So the response may be too general to be useful and it may not fit their desired tone or the audience. Right? So instead of that you can write generate a 100 word sales page for an AI powered fitness app focusing on personalization and efficiency. So now you guys will be wondering why this is better than this prompt. So the here you can see the product is specified AI powered fitness app and it defines the length of 100 words to keep it concise and it highlighting the key selling point personalization and efficiency. Right? Let's run it. So if the response is not exactly what you need, you can refine it further. Like make the sales page more persuasive by including a specific offer, a special offer. Rewrite the page with more engaging conversation tone. You can write here like this. Rewrite the page more. So here you can see tired of getting work your workouts need this your personal AI fitness school that tailor workouts better than this. Right? So you can refine it for further as per your need. Right? So final takeaways are you have to be specific keep it focused specify formatting refine and iterate. So by avoiding these common errors and applying the fixes, you can improve the accuracy, clarity and usefulness of your AI generated content. So now let's move forward and dive into some advanced techniques of prompt engineering. So in this module we will talk about some advanced topics or you can say advanced techniques of prompt engineering. So there are some advanced techniques like zero short, few short and chain of thought prompting. So we will go through each technique with detailed explanation. Okay. So when working with AI language models, the way a prompt is structured significantly influences the response quality. So to optimize results so there are three key points techniques are commonly used which are zeros short learning few short learning and chain of thought prompting. So each technique has a unique approach to guiding AI in generating relevant and accurate responses. So let's first start with zeros short learning. So let me write here zero short learning. Okay. So zeros short learning refers to a scenario where an AI model is asked to generate a response without any prior example or guidance. So the AI or shared GP entirely rely on its pre-trained knowledge to provide an answer based on the prompt alone. Okay. So this technique is used when an AI model is expected to generalize and generate responses without a specific example. For example, let me write. So, so this is the example of zero short learning. Okay. So, here is the prompt like explain the importance of cyber security and businesses. Okay. So here response will be it's like a cyber security is crucial for business because it helps. Okay. So yeah. So now how zeros learning works? So zero short learning is comparable to asking someone to answer a question on a topic. So they have read about but without any additional instruction the charge GPT or AI must infer the correct response purely from its training data and knowledge. Right? So what are the advantages of zero short learning? So it is very quick and efficient which can like AI can generate responses immediately without additional context. And the second point is works for journal knowledge question. So AI provides reasonable answer based on its pre-trained model. The third advantage is it's very very ideal for open-ended queries like it is great for broad topics like explain the importance of cyber security in business. Okay. So it is very great for broad topics where detail explanation are not necessary. Right. So there are some challenges as well of zeros short learning. It's like the AI may generate generic or less precise responses. It lacks a specific guideline. Okay. And second thing is it will you know complex task may require more context for better activism. Right. And let's go for one more example of zero short learning which will be describe how blockchain technology and it's impact on financial security. Okay. So this is the AI model generated a response without any prior example or guidance. Okay. With its knowledge, right? So yeah. So now let's move forward and see what is few short learning. Okay. Let me write here few short learning. Okay. So few short learning enhances AI performance by providing a few example. Okay, that's why it's called few short learning. So before asking it to generate a response, it will ask we will give the few example. Okay. So this approach helps AI recognize patterns and produce more relevant, structured and accurate responses. So it is very useful when training AI to follow a specific tone, structure or expected answer form. Okay. What if you need a particular answer in poem style? Okay. So that or in that way you can use f short learning. Okay. So let me give one example here. So first let me give one prompt. shall give example one I will write strong password contains upper case oops and symbol and I will write here example example authentication. Add extra extra layer of security. Fine. Then I will write here now exclamation software type in 200 and I don't need this much of okay center. So how few short learning works is like few short learning is similar to how people learn from examples and analogies. Okay. So here already given two examples this one and this second this is the second example and after that I'm asking the question. So by providing reference example before the final question so AI will understand what is expected. So now it will give me keeping software updated is crucial this this list this for example see if an operating system or web browser has security local attackers can use it to inject malicious code to exclude ransomware attacks like okay so just using as a strong password and two factor authenticator add layer layers of security regular software update So here you can see a strong password and two factor as okay. So that's why few short learning is very very great. So what are the advantage of few short learning? Okay. So this is the more accurate and contextual responses. So AI will learn from the example and follows the pattern. So this is better for specific use case. Here the use cases software and answer security. Okay. and future learning enhances AI comprehension. So the AI can generalize better when given structured prompt. So there are some challenges as well. Okay. So it requires carefully chosen example to ensure AI understand the desired output. So the your examples or references must be good enough to understand what you want. Right? That's why prompt engineering is very very crucial and by giving more examples may lead to longer processing times for generating responses. Okay. But nowadays JP is very very smart. So it won't take much longer time processing times. So yeah but by giving two three example are fine I guess right. So now let's talk about third advanced technique which is chain of thoughts. Okay. So what is chain of thought prompt? So chain of thought prompting helps AI break down complex task into logical steps. Right? So this method is particularly effective for reasoning based stuff where AI needs to explain processes or solve problems step by steps like here explain explain the process of photosynthe This is three steps. Okay, I don't want detailed steps. Okay, I just need any steps. First it will be tell about photosynthesis and then it will start to do steps in three steps it will tell. So how chain of thing works? So instead of asking AI to provide a direct answer, chain of thought prompting encourages AI to reason through a problem or explanation in multiple stages. Okay. So this approach improves the clarity and helps AI generate well structured responses. So now you can see here the first step is this, second step, third step. So it is very very easy for us as well to understand the answer. Okay. Generated answer. So now you even you can ask like describe how am electric. So mistake. Yeah. Press enter. Okay. Small definition. first step, second step and the third step. So this is very easy for us as well to understand right. If it will give in long long paras like this how you will understand right. So that's why you should know how to use chain of thought prompting. Okay. So there are advantages as well like it enhances logical reasoning. It provides step-by-step explanation for better understanding ideal for complex topics. It is very very useful for explaining processes solving math problem or reasoning through difficult concepts and it encourages AI to think structurally. So instead of a single response AI processes the problem in a logical sense okay in logical sequence you can. So if there are advantage so there are challenges as well. So sometimes responses can be lengthy and timeconuming to process and AI needs clear prompts to break down information effectively. So each prompting technique serves different use cases. So zero short learning you can use when AI must generate responses quickly without any guidance and you can use few short learning when AI needs to follow a specific pattern or structure and use you can use chain of thought prompting when AI must reason through a problem or explain a process in this steps. So by understanding and applying these techniques correctly, users can optimize AI performance for various tasks from answering general question to generating structured content and solving complex problems. Right? So prompt engineering is very essential skill that analysis AI interactions. So by understanding components, avoiding common mistakes and applying structure technique, user can optimize AI generated content. So mastering various prompting techniques like zero short few short and chain of thought it will improves accuracy and efficiencies in AI application and your generated content. So continue practice and refine your prompts to become proficient in leveraging AI for diverse task. So by integrating these exercises and real world case studies and expert insight. So congratulation on completing the chat GPT for prompt engineering course. So you have taken a big step in leveraging AI to enhance your prompt engineering skills. So now it's time to put your learning into act integrating your prompt engineering skills into your workflows and experiment with prompts to optimize project efficiency. Hey everyone, welcome to SimplyLearn's YouTube channel. In this tutorial, we will learn how AI can assist everyone in their daily life. Before that, let's take a look at why we should consider AI for daily tasks. In our daily lives, we often find ourselves stuck in time- conssuming and repetitive tasks, writing endless emails, scheduling meetings, sorting out data, or brainstorming ideas only to start over again. These activities, while necessary, can drain productivity and creativity. This is where AI steps in. Transforming the way we work and live. AI can draft emails, credit grammar, and summarize messages in seconds. It streamlines meeting planning by finding the best time slots, sending invites, and setting reminders. In marketing, AI suggests and analyzes trends for fresh ideas, saving hours of research. Automation tools handle tedious tasks like data entry and invoicing, freeing up more time for more meaningful work. AI covered chat bots offer instant customer support while voice assistants manage everyday tasks like setting alarms and making calls. It even organizes to do lists, prioritizes tasks and recommends product based on preferences. Translation tools break language barriers and smartome devices adjust lighting, temperature and security effortlessly. AI isn't just a tool. It's a digital assistant designed to enhance productivity and simplify life. That said, if these are the type of videos you'd like to watch, then hit that like and subscribe buttons and the bell icon to get notified. Now, the agenda for today's session. Firstly, let's get started with the agenda for today's session. Now, we will have a brief introduction to this session. Followed by that, we will have a briefing of what exactly is AI, then how does it work. Next we will understand the key technologies and frameworks involved and we will also go through some everyday use cases where we can take help of AI and also some of the responsibilities that we need to keep in mind for the ethical use of AI and lastly we will explore some prompts. Now that I have made myself clear with the agenda let's get started with the first part introduction. How can AI simplify daily task? As we discussed in the use case, a human might have a lot of routine and mundane tasks. For example, automation. AI can simplify your daily tasks like checking up emails and drafting email responses, enhancing